Apple slice grading is useful in post-harvest operations for sorting, grading, packaging, labeling, processing, storage, transportation, and meeting market demand and consumer preferences. Proper grading of apple slices can help ensure the quality, safety, and marketability of the final products, contributing to the post-harvest operations of the overall success of the apple industry. The article aims to create a convolutional neural network (CNN) model to classify images of apple slices after immersing them in atmospheric plasma at two different pressures (1 and 5 atm) and two different immersion times (3 and again 6 min) once and in filtered water based on the hardness of the slices using the k-Nearest Neighbors (KNN), Tree, Support Vector Machine (SVM), and Artificial Neural Network (ANN) algorithms. The results showed an inverse relationship between the storage period and the hardness of the apple slices, with the average hardness values gradually decreasing from 4.33 (day 1) to 3.37 (day 5). Treatment with atmospheric plasma at a pressure of 5 atm and an immersion time of 3 min gave the best results for maintaining the hardness of the slices during the storage period, recording values of 4.85 (first day) and 3.68 (fifth day), outperforming other treatments. The average improvement rate was 23.09% over five consecutive days. Regarding the CNN algorithms, the ANN algorithm achieved the highest classification accuracy of 97%, while the Tree algorithm achieved the lowest accuracy of 88.7%. The KNN and SVM algorithms achieved classification accuracies of 94.7% and 95.1%, respectively. The study demonstrated the possibility of using a CNN to classify apple slices based on the degree of hardness. Furthermore, the application of atmospheric plasma at 5 atmospheres with a 3-min immersion improves the firmness of the apple slices by inhibiting degradative enzymes while preserving the cellular structure and tissue quality.
In this study, the response of ten composite post-tensioned concrete beams topped by a reinforced concrete deck with adequate reinforcing shear connectors is investigated. Depending on the concrete compressive strength of the deck slab (20, 30, and 40 MPa), beams are grouped into three categories. Seven of these beams are exposed to a fire attack of 700 and 800 °C temperature simultaneously with or without the presence of a uniformly distributed sustained static loading. After cooling back to ambient temperature, these composite beams are loaded up to failure, using a force control module, by monotonic static loading in a four-point-bending setup with two symmetrical concentrated loads applied in
In this study, the response of ten composite post-tensioned concrete beams topped by a reinforced concrete deck with adequate reinforcing shear connectors is investigated. Depending on the concrete compressive strength of the deck slab (20, 30, and 40 MPa), beams are grouped into three categories. Seven of these beams are exposed to a fire attack of 700 and 800 °C temperature simultaneously with or without the presence of a uniformly distributed sustained static loading. After cooling back to ambient temperature, these composite beams are loaded up to failure, using a force control module, by monotonic static loading in a four-point-bending setup with two symmetrical concentrated loads applied in
A low-cost reverse flow plasma system powered by argon gas pumping was built using homemade materials in this paper. The length of the resulting arc change was directly proportional to the flow rate, while using the thermal camera to examine the thermal intensity distribution and demonstrating that it is concentrated in the centre, away from the walls at various flow rates, the resulting arc's spectra were also measured. The results show that as the gas flow rate increased, so did the ambient temperature. The results show that the medium containing the arc has a maximum temperature of 34.1 ˚C at a flow rate of 14 L/min and a minimum temperature of 22.6 ˚C at a flow rate of 6 L/min.
A method has been demonstrated to synthesise effective zeolite membranes from existing crystals without a hydrothermal synthesis step.
This research presents a model for surveying networks configuration which is designed and called a Computerized Integrated System for Triangulation Network Modeling (CISTNM). It focuses on the strength of figure as a concept then on estimating the relative error (RE) for the computed side (base line) triangulation element. The CISTNM can compute the maximum elevations of the highest
obstacles of the line of sight, the observational signal tower height, the contribution of each triangulation station with their intervisibility test and analysis. The model is characterized by the flexibility to select either a single figure or a combined figures network option. Each option includes three other implicit options such as: triangles, quadri
Background: In the present study used device jet plasma needle with atmospheric pressure which generates non thermal plasma jet to measure treatment potent with plasma against pathogenic bacteria founded in UTI was inactivated with plasma at 10 sec,
Objective:. This work included the application of the plasma produced from the system in the field of bacterial sterilization , where sample of Gram- negative bacteria (Escherichia coli) were exposed to intervals (1-10)second . Midstream Urine samples swabs were obtained from patients with urinary tract infections.
Type of the study: Cross -sectional study.
Methods: The work were used i
... Show MoreThe successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi
... Show MoreA content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a
... Show MoreIn this study, a Hydroxyapatite (HA) coating was prepared on a titanium implant by an electrochemical deposition process. The titanium pre-treatment by anodizing in 1.65 mol/L sulfuric acid with (10V) at room temperature. The deposition was all conducted at a constant voltage of 6.0 V, for 1 h at room temperature. The coatings thus prepared were characterized with Fourier transform infrared spectroscopy (FTIR) and thickness of the coated layer.The electrochemical deposition of HA occurred on the titanium as a cathode. Coated titanium by HA after anodizing revealed a good corrosion protection efficiency even at a temperature ranged (293-323) K in artificial saliva. Activation energy and pre-exponential factor (kinetic parameters) were calcul
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